Data privacy is one of the hotspots with various privacy-preserving solutions being proposed in terms of designing privacy-aware machine learning algorithms and platforms [1]}, [2]}, [3]}, [4]} or implementing cryptographic algorithms, such as homomorphic encryption, to encrypt the dataset [5]} prior to a data disclosure.
However, the privacy-aware machine learning algorithms address only the “hidden trees in a forest” problem by obfuscating the query results, and cannot prevent data leakage. On the other hand, homomorphic encryption is computationally intensive  [6]} and prohibitive for edge applications, and has to be carefully crafted in respect to different learning algorithms with weak generality [7]}.
